class_docstr: Calculate the confusion matrix of the model on the given dataset.
  
  Parameters
  ----------
  normalize_display : bool , default: True:
      boolean that determines whether to normalize the values of the matrix in the display.
  n_samples : int , default: 10_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
docstr: Add condition - Misclassified samples lower than threshold.
  
  Condition validates if the misclassified cell size/samples are lower than the threshold based on the
  `misclassified_samples_threshold` parameter.
  
  Parameters
  ----------
  misclassified_samples_threshold: float, default: 0.20
      Ratio of samples to be used for comparison in the condition (Value should be between 0 - 1 inclusive)
docstr_tok: ["Add","condition","-","Misclassified","samples","lower","than","threshold",".","Condition","validates","if","the","misclassified","cell","size\/samples","are","lower","than","the","threshold","based","on","the","`","misclassified_samples_threshold","`","parameter",".","Parameters","--","--","--","--","--","misclassified_samples_threshold",":","float",",","default",":","0.20","Ratio","of","samples","to","be","used","for","comparison","in","the","condition","(","Value","should","be","between","0","-","1","inclusive",")"]
code: def add_condition_misclassified_samples_lower_than_condition(
      self, misclassified_samples_threshold: float = 0.2
  ):
      """Add condition - Misclassified samples lower than threshold.
  
      Condition validates if the misclassified cell size/samples are lower than the threshold based on the
      `misclassified_samples_threshold` parameter.
  
      Parameters
      ----------
      misclassified_samples_threshold: float, default: 0.20
          Ratio of samples to be used for comparison in the condition (Value should be between 0 - 1 inclusive)
      """
      return self.add_condition(
          f"Misclassified cell size lower than {format_percent(misclassified_samples_threshold)} "
          "of the total samples",
          misclassified_samples_lower_than_condition,
          misclassified_samples_threshold=misclassified_samples_threshold,
      )
  
file_path: deepchecks/deepchecks/nlp/checks/model_evaluation/confusion_matrix_report.py
imports: import numpy
  from deepchecks.core import CheckResult
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.utils.abstracts.confusion_matrix_abstract import misclassified_samples_lower_than_condition, run_confusion_matrix_check
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 3 for calling the ConfusionMatrixReport obj's underlying member method code with example usage: obj.add_condition_misclassified_samples_lower_than_condition(misclassified_samples_threshold) and returns: self